Challenge 9

Loading in libraries/dataset

Code

library(tidyverse)
library(here)
library(broom)
library(knitr)
library(kableExtra)
library(DT)
library(scales)

Code

babyNames <- read_csv(here::here("supporting_artifacts", "Extended Thinking", 
                                 "Challenge 9", "StateNames_A.csv")) |> 
  rename(Sex = "Gender")

1. Interactive dataset for babyNames

Code

datatable(babyNames)

2a. Summarizing & Visualizing the Number of Allisons - All

Code

babyNames |> 
  group_by(Sex, State) |> 
  filter(Name == "Allison") |> 
  summarize(Total = sum(Count)) |>
  pivot_wider(names_from = Sex, values_from = Total) |> 
  mutate(M = replace_na(M, 0), `F` = replace_na(`F`, 0)) |> 
  kable(caption = "<center><strong>Frequencies of babies named 
        Allison by state</strong></center>") |> 
  kable_styling(bootstrap_options = "striped", html_font = "Cambria") 

Frequencies of babies named Allison by state

State	F	M
AK	232	0
AL	1535	0
AR	1198	0
AZ	1880	0
CA	12413	0
CO	1594	0
CT	1099	0
DC	321	0
DE	294	0
FL	4455	0
GA	3257	0
HI	183	0
IA	1477	0
ID	451	0
IL	5110	0
IN	3067	0
KS	1283	0
KY	1905	20
LA	1209	0
MA	2218	0
MD	2229	0
ME	340	0
MI	4014	0
MN	2374	0
MO	2882	0
MS	817	0
MT	226	0
NC	3435	0
ND	285	0
NE	807	0
NH	412	0
NJ	3052	0
NM	399	0
NV	729	0
NY	5747	0
OH	5487	0
OK	1421	0
OR	1186	0
PA	4307	0
RI	306	0
SC	1228	0
SD	376	0
TN	2488	0
TX	10192	0
UT	1125	0
VA	3220	0
VT	135	0
WA	1956	0
WI	2367	0
WV	813	0
WY	142	0

2b. Summarizing & Visualizing the Number of Allisons - Female

Code

babyNames |> 
  mutate(Count = replace_na(Count, 0)) |> 
  group_by(Sex, State) |> 
  filter(Name == "Allison", Sex == "F") |> 
  summarize(Total = sum(Count)) |>
  kable(caption = "<center><strong>Frequencies of female babies named 
        Allison by state</strong></center>") |> 
  kable_styling(bootstrap_options = "striped", html_font = "Cambria") 

Frequencies of female babies named Allison by state

Sex	State	Total
F	AK	232
F	AL	1535
F	AR	1198
F	AZ	1880
F	CA	12413
F	CO	1594
F	CT	1099
F	DC	321
F	DE	294
F	FL	4455
F	GA	3257
F	HI	183
F	IA	1477
F	ID	451
F	IL	5110
F	IN	3067
F	KS	1283
F	KY	1905
F	LA	1209
F	MA	2218
F	MD	2229
F	ME	340
F	MI	4014
F	MN	2374
F	MO	2882
F	MS	817
F	MT	226
F	NC	3435
F	ND	285
F	NE	807
F	NH	412
F	NJ	3052
F	NM	399
F	NV	729
F	NY	5747
F	OH	5487
F	OK	1421
F	OR	1186
F	PA	4307
F	RI	306
F	SC	1228
F	SD	376
F	TN	2488
F	TX	10192
F	UT	1125
F	VA	3220
F	VT	135
F	WA	1956
F	WI	2367
F	WV	813
F	WY	142

3. Alan dataset

Code

A_names <- babyNames |> 
  filter(Name %in% c("Alan", "Allen", "Allan"), Sex == "M")

4. Interactive dataset for A_names

Code

datatable(A_names)

5. Total Counts

Code

A_names |> 
  filter(State %in% c("CA", "PA"), Year == "2000") |> 
  pivot_wider(names_from = Name, values_from = Count) |> 
  mutate(across(Alan:Allan, .fns = replace_na, 0)) |> 
  rowwise() |> 
  mutate(Total = sum(Alan, Allen, Allan)) |> 
  select(-c(Year, Sex)) |> 
  kable(caption = "<center><strong>Total counts of Different Alan 
        spellings in CA and PA in 2000</strong></center>") |> 
  kable_styling(bootstrap_options = "striped", html_font = "Cambria") 

Total counts of Different Alan spellings in CA and PA in 2000

State	Alan	Allen	Allan	Total
CA	579	176	131	886
PA	51	56	12	119

6. Percentages

Code

A_names |> 
  filter(State %in% c("CA", "PA"), Year == "2000") |> 
  pivot_wider(names_from = Name, values_from = Count) |> 
  mutate(across(Alan:Allan, .fns = replace_na, 0)) |> 
  rowwise() |> 
  mutate(total = sum(Alan, Allen, Allan), Alan = percent(Alan/total), 
         Allen = percent(Allen/total), 
         Allan = percent(Allan/total)) |> 
  select(-c(total, Year, Sex)) |> 
  kable(caption = "<count><strong>Percentages of different Alan 
        spellings in CA and PA in 2000 rounded 
        to nearest whole number</strong></center>", 
        digits = 2) |> 
  kable_styling(bootstrap_options = "striped", html_font = "Cambria") 

Percentages of different Alan spellings in CA and PA in 2000 rounded to nearest whole number

State	Alan	Allen	Allan
CA	65%	20%	15%
PA	43%	47%	10%